Standard Backprop
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-06.)
Standard Backprop has 8 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Mostly:step includes(3), requires backward pass(1), step1(1)
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Other facts (8)
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| Predicate | Value | Ref |
|---|---|---|
| Step Includes | Forward Pass | [2] |
| Step Includes | Backward Pass | [2] |
| Step Includes | Gradient Update | [2] |
| Requires Backward Pass | true | [1] |
| Step1 | Forward pass → compute loss | [1] |
| Step2 | nn.value_and_grad() → autodiff backward pass through every layer | [1] |
| Step3 | Gradients tell each parameter exactly which direction reduces loss | [1] |
| Step4 | Update parameters accordingly | [1] |
Timeline
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References (2)
ctx:discord/blah/watt-activation/part-122ctx:discord/blah/watt-activation/122- full textwatt-activation-122text/plain3 KB
doc:agent/watt-activation-122/57649dd0-cec5-4d9a-bc09-bec5f2db2137Show excerpt
[2026-03-09 01:19] xenonfun: ⏺ BP = Backpropagation — whether the optimizer computes gradients via automatic differentiation or not. Adam / RotAdamW use standard backprop: 1. Forward pass → compute loss 2. nn.value_and_grad() → autod…
See also
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